SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This presentation provides an overview of the laboratory evaluation of federated, hierarchical controls for distribution power system management.
This article presents a modular ac/dc system with both distributed and centralized power ports for energy router (ER) applications. In each module of the described system, photovoltaic (PV) power generation units, battery-type energy storage (ES) units, and critical loads are connected to the cascaded H-bridge (CHB)-organized medium-voltage (MV) dc links, with fully distributed low-voltage (LV) dc power ports. Copies of modules share the centralized load bus and interact with an MV ac grid in parallel. Hybrid power port (HPO) assigns flexibility to the system but makes energy routings a necessity for stable operation. In this article, a segmented energy management strategy for the HPO-ER is proposed. In terms of the grid-side power transferring, the system ratings are intentionally designed to match significant power imbalance. Focally, a segmented energy management strategy is proposed to realize fully autonomous energy routing involving MV ac grid, distributed PV generations, distributed battery storages, and LV load. The system is proven to be stable using the derived impedance-based model considering the interaction between power blocks. The feasibility of the topology and control strategy is also verified through software simulation, laboratorial hardware prototype experiment, and hardware-in-the-loop (HIL) emulation.
Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.
Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).
Kynema-FMB (FKA: Kynema) is an open-source performance portable flexible multibody (FMB) dynamics solver designed for time-domain simulations. While originally tailored for wind turbine structural dynamics, the formulation and implementation are those of a general flexible-multidbody dynamics solver that can readily be applied to a wide range of systems. Kynema was designed with a narrow focus, namely to provide a lightweight, fast, accurate FMD solver for coupling to computational-fluid-dynamics (CFD) codes, especially the CFD codes in the Kynema suite, for fluid-structure-interaction (FSI) simulations. Kynema-FMB is equipped to model systems that can be represented as a collection of beams and rigid bodies that are connected through constraints. Degrees of freedom are defined in the inertial/global frame of reference and include displacements and rotations (formally as rotation matrices, but stored as quaternions). The underlying formulation is built on a Lie-group time integrator designed for index-3 differential-algebraic equations, which is second-order accurate in time (Bruls et al., 2012). Beam models are based on geometrically exact beam theory and are discretized as high-order spectral finite elements similar to those in BeamDyn (Wang et al., 2017). The governing equations for a FMD system like a wind turbine constitute a highly nonlinear system of constrained partial-differential equations. Kynema-FMB uses analytical Jacobians in the nonlinear-system solves in each time step. Linear systems use sparse storage and several third-party sparse-linear-system solvers are enabled. Ill conditioning of linear systems is mitigated with preconditioning described in Bottasso et al, 2008. Kynema-FMB is integrated with a simple open-source controller (ROSCO). There is an application programming interface (API) for coupling to geometry-resolved CFD (like that in Sharma et al., 2023) and actuator-force CFD (like that in Kuhn et al., 2025). In the latter, for actuator-line models, Kynema-FMB includes an internal blade-element solver that depends on user-provided lookup tables for coefficients of lift and drag, i.e., aerodynamic polars. Kynema-FMB is written in C++ and leverages Kokkos and Kokkos-Kernels (KokkosEcosystem) as its performance portability layer enabling simulations on both CPU and GPU systems. The repository is equipped with extensive automated testing at the unit and regression/system levels. The following describes the high-level development objectives conceived for Kynema: *Kynema will follow modern software development best practices, including test-driven development (TDD), version control, hierarchical automated testing, and continuous integration (CI) for a robust development environment. *The core data structures are memory efficient and enable vectorization and parallelization at multiple levels. *Data structures are data-oriented to exploit methods for accelerated computing including high utilization of chip resources (e.g., single instruction multiple data (SIMD) instruction sets) and parallelization using GP-GPUs. *The computational algorithms incorporate robust open-source libraries for mathematical operations, resource allocation, and data management. *The API design considers multiple stakeholder needs and ensure integration with existing and future ecosystems for data science, machine learning, and AI. *Kynema-FMB is written in modern C++ and leverages Kokkos as its performance-portability library with inspiration from the kynema stack.
SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.
Distributed energy resources (DERs), including rooftop solar, energy storage, and flexible loads, are gaining popularity as costs decline and as building owners and utilities realize their benefits. DERs can improve distribution system efficiency, help prevent the need for expensive grid upgrades, and increase the resilience of local communities. However, they can also cause difficulties in grid operations and can require controls to achieve their benefits. To address this challenge, NREL researchers have developed a community-scale solution that assesses the impacts of DERs and their control strategies on a distribution system. The framework has been shown to reduce solar photovoltaic (PV) curtailment to 0%, mitigate the adverse impact of solar variability on the distribution voltage, and provide up to 5-day critical load support during emergency events. Utilizing 5 different modules representing the feeder, buildings, home energy management systems, an aggregator, and a utility controller, NREL expects this simulation technology to play a critical role in the continued integration of DERs. According to the Energy Information Administration (EIA), solar curtailments accounted for 94% of the total energy curtailed in the California Independent System Operator (CAISO) in 2020. By enabling Independent System Operators (ISOs) and utility operators to bring solar curtailments to 0%, the electrical grid can become less dependent on fossil-fueled power generation sources. NREL's co-simulation framework contains five major components: Distribution Feeder Model: describes the distribution feeder topology using OpenDSS, including the locations of all DERs. Residential Building Model: simulates a large number of buildings at a high resolution using OCHRETM. The model is equipped to control equipment based on signals from an external module. The model includes major household appliances such as HVAC and a water heater, non-dispatchable load models, a distributed PV system, and a home battery system. Home Energy Management System: optimizes the controls for the devices in a home using foreseeTM. The control can adjust based on the user preferences including cost, comfort, and convenience. In hierarchical control scenarios, where the houses follow signals from an aggregator, the home energy management system provides a flexibility band with a range of power and follows the dispatch signals received from aggregator. Community-Level Aggregator: solves for optimal energy dispatch based on the flexibility bands received from each home and the grid service signal received from the utility controller. Utility-Level Controller: provides grid signals for voltage control using Distributed Energy Resources (DERs), such as solar systems, in the community.